In the medical industry there is often the need for an experienced laboratory technician to review a specimen of biological matter for the presence of cells of a certain cellular type. An example of this is the need to review a pap smear slide for the presence of malignant or premalignant cells. A pap smear often contains as many as 100,000 to 200,000 or more cells and other objects, each of which a technician must individually inspect in order to determine the possible presence of very few malignant or premalignant cells. Pap smear tests, as well as other tests requiring equally exhausting cell inspection techniques, have therefore suffered from a high false negative rate due to the tedium and fatigue imposed upon the technician.
Several thousand women die each year in the United States alone from cervical cancer; a cancer from which a woman theoretically has a high probability of survival if detected in its early in situ stages. If not detected early, however, the chances of survival may decrease drastically. If a malignant cell in a pap smear is missed, by the time the woman has another pap smear performed the cancer may have advanced to its invasive stage from which a woman has a much smaller chance of survival. Consequently, the importance of detecting the presence of only one or a few malignant or premalignant cells among the hundreds of thousands of cells in a smear cannot be overstated. Unfortunately, present manual screening methods are inaccurate. In fact, recently some laboratories have been found to have incorrectly classified as benign up to 30% of the specimens containing malignant or premalignant cells. Also unfortunate is that many prior attempts to automate the cell inspection or classification have been unsuccessful.
Predominately, these prior attempts at automation have relied on feature extraction, template matching and other statistical or algorithmic methods alone. These attempts have required expensive and time-consuming cell preparations to distribute the cells and other objects over a slide so that none of the cells or objects overlap. However, even then these attempts have been unsuccessful at accurately classifying specimens in a reasonable time frame.
These difficulties have been overcome by combining an algorithmic or statistical primary classifier with a neural network based secondary classifier as disclosed in U.S. Pat. No. 4,965,725, and U.S. patent application Ser. Nos. 07/420,105, 07/425,665, 07/502,611 and 07/610,423, which are incorporated in their entireties by this reference. A commercially available automated pap smear screener, using a primary classifier in conjunction with a neurocomputer based secondary classifier is produced by Neuromedical Systems, Inc..RTM. of Suffem, New York under trademark PAPNET.TM..